Cognitive Correlates of Anterolateral Entorhinal Cortex Volume Differences in Older Adults
Bibliographic record
Abstract
Alzheimer’s disease pathology first appears in the medial temporal lobe (MTL): in particular, in the anterolateral region of the entorhinal cortex (alERC). However, the scientific community has only recently begun to appreciate the importance of the subdivision of the human entorhinal cortex, and as such, our understanding of the alERC’s cognitive roles and how it fits into models of MTL function remains limited. In this dissertation, I describe a series of studies inspired by findings from the animal literature, which shed light on the cognitive functions supported by the alERC. Using structural volumetry, I quantified the volumes of the alERC and other MTL regions in a group of ostensibly healthy older adults with varying degrees of cognitive decline. I compared participants’ structural differences to their performance on behavioral tasks that we hypothesized represented cognitive functions supported by the alERC. Specifically, I investigated how alERC structural differences were related to performance on the Montreal Cognitive Assessment (MoCA), a standard neuropsychological assessment used in the diagnosis of AD (Chapter 2), as well as eyetracking-based behavioral tasks assessing intra-item configural processing (Chapter 3) and object-in-place memory (Chapter 4), cognitive functions which the rodent literature suggest are supported by the alERC. I found that participants who scored below the MoCA threshold score (indicating possible AD) had smaller alERC volumes (Chapter 2), demonstrating that alERC volume is related to cognitive decline. Further, intra-item configural processing – regardless of an object’s novelty – was strongly predicted by alERC volume, but not by the volume of any other MTL subregion (Chapter 3). Finally, alERC (and parahippocampal cortex) volume was also related to object-in-place memory for items in everyday scenes, but no such relationship was found for object-trace memory (Chapter 4). Together, these studies suggest that the alERC may support aspects of the spatial processing of objects, advancing our understanding of the cognitive correlates of the alERC. More pragmatically, the alERC-specific tasks I developed may prove useful in early screening for AD and evaluating its progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".